Using DevExpress Chat Component and Semantic Kernel ResponseFormat to show a product carousel
Today, when I woke up, it was sunny but really cold, and the weather forecast said that snow was expected.
So, I decided to order ramen and do a “Saturday at home” type of project. My tools of choice for this experiment are:
1) DevExpress Chat Component for Blazor
I’m thrilled they have this component. I once wrote my own chat component, and it’s a challenging task, especially given the variety of use cases.
2) Semantic Kernel
I’ve been experimenting with Semantic Kernel for a while now, and let me tell you—it’s a fantastic tool if you’re in the .NET ecosystem. It’s so cool to have native C# code to interact with AI services in a flexible way, making your code mostly agnostic to the AI provider—like a WCF for AIs.
Goal of the Experiment
The goal for today’s experiment is to render a list of products as a carousel within a chat conversation.
Configuration
To accomplish this, I’ll use prompt execution settings in Semantic Kernel to ensure that the response from the LLM is always in JSON format as a string.
var Settings = new OpenAIPromptExecutionSettings
{
MaxTokens = 500,
Temperature = 0.5,
ResponseFormat = "json_object"
};The key part here is the response format. The chat completion can respond in two ways:
- Text: A simple text answer.
- JSON Object: This format always returns a JSON object, with the structure provided as part of the prompt.
With this approach, we can deserialize the LLM’s response to an object that helps conditionally render the message content within the DevExpress Chat Component.
Structure
Here’s the structure I’m using:
public class MessageData
{
public string Message { get; set; }
public List Options { get; set; }
public string MessageTemplateName { get; set; }
}
public class OptionSet
{
public string Name { get; set; }
public string Description { get; set; }
public List Options { get; set; }
}
public class Option
{
public string Image { get; set; }
public string Url { get; set; }
public string Description { get; set; }
};- MessageData: This structure will always be returned by our LLM.
- Option: A list of options for a message, which also serves as data for possible responses.
- OptionSet: A list of possible responses to feed into the prompt execution settings.
Prompt Execution Settings
One more step on the Semantic Kernel side is configuring the prompt execution settings:
var Settings = new OpenAIPromptExecutionSettings
{
MaxTokens = 500,
Temperature = 0.5,
ResponseFormat = "json_object"
};
Settings.ChatSystemPrompt = $"You need to answer using this JSON format with this structure {Structure} " +
$"Before giving an answer, check if it exists within this list of option sets {OptionSets}. " +
$"If your answer does not include options, the message template value should be 'Message'; otherwise, it should be 'Options'.";In the prompt, we specify the structure {Structure} we want as a response, provide a list of possible options for the message in the {OptionSets} variable, and add a final line to guide the LLM on which template type to use.
Example Requests and Responses
For example, when executing the following request:
- Prompt: “Show me a list of Halloween costumes for cats.”
We’ll get this response from the LLM:
{
"Message": "Please select one of the Halloween costumes for cats",
"Options": [
{"Image": "./images/catblack.png", "Url": "https://cat.com/black", "Description": "Black cat costume"},
{"Image": "./images/catwhite.png", "Url": "https://cat.com/white", "Description": "White cat costume"},
{"Image": "./images/catorange.png", "Url": "https://cat.com/orange", "Description": "Orange cat costume"}
],
"MessageTemplateName": "Options"
}With this JSON structure, we can conditionally render messages in the chat component as follows:
<DxAIChat CssClass="my-chat" MessageSent="MessageSent">
<MessageTemplate>
<div>
@{
if (@context.Typing)
{
<span>Loading...</span>
}
else
{
MessageData md = null;
try
{
md = JsonSerializer.Deserialize<MessageData>(context.Content);
}
catch
{
md = null;
}
if (md == null)
{
<div class="my-chat-content">
@context.Content
</div>
}
else
{
if (md.MessageTemplateName == "Options")
{
<div class="centered-carousel">
<Carousel class="carousel-container" Width="280" IsFade="true">
@foreach (var option in md.Options)
{
<CarouselItem>
<ChildContent>
<div>
<img src="@option.Image" alt="demo-image" />
<Button Color="Color.Primary" class="carousel-button">@option.Description</Button>
</div>
</ChildContent>
</CarouselItem>
}
</Carousel>
</div>
}
else if (md.MessageTemplateName == "Message")
{
<div class="my-chat-content">
@md.Message
</div>
}
}
}
}
</div>
</MessageTemplate>
</DxAIChat>End Solution Example
Here’s an example of the final solution:
You can find the full source code here: https://github.com/egarim/devexpress-ai-chat-samples and a short video here https://youtu.be/dxMnOWbe3KA
